mirror of
https://github.com/Sea-Haven-Industries/apm-wo-analysis.git
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Add two-axis classifier Lambda and CDK wiring (Phase 2)
Implement the core classification engine and wire it into the pipeline stack. classify.py: two-axis classifier — HTML-strip, comment-intent regex buckets (escalations → status inquiry, most-specific first), Hold Reason / WO Status structured state, comment-vs-state mismatch detector, and a Claude Haiku fallback (Secrets Manager key) reserved for ambiguous free-text. Exports ESCALATION_CATEGORIES / ACTION_NEEDED_CATEGORIES. handler.py: S3-triggered handler — parse xlsx/csv, classify each non-blank row, write a per-WO Parquet snapshot to analytics/dt=YYYY-MM-DD/ (registers the Glue partition via awswrangler) and a summary.json for slack-post (Phase 4). pipeline_stack.py: Glue database, ARM64 Python 3.12 classifier Lambda (Docker-bundled deps), S3 raw/ notification (.xlsx/.csv), and least-privilege IAM (read raw/, read-write analytics/, scoped Glue catalog, read Anthropic key). Smoke-tested against the real export: 347 rows, "Other" at 5.2% (target ~9%), 18 mismatches flagged. 7/7 unit + smoke tests pass; cdk synth green.
This commit is contained in:
Adam Moussa
parent
7befc8f0d7
commit
eb724580e0
4 changed files with 1045 additions and 32 deletions
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@ -1,27 +1,50 @@
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"""Pipeline stack: S3, classifier + slack-post Lambdas, Glue, Athena, IAM.
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Scaffold — the exports bucket (Phase 1 of docs/BUILD.md) is included so the
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stack synthesizes to something real. The classifier Lambda (Phase 2), Glue
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database + Athena workgroup with partition projection (Phase 3), and the
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slack-post Lambda + IAM (Phase 4) are added in their respective phases.
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Phase 1 — exports bucket + drop-folder uploader IAM user.
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Phase 2 — Glue database + S3-triggered classifier Lambda (this file).
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Phase 3 — Athena workgroup + partition-projection table (TODO).
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Phase 4 — slack-post Lambda + scoped IAM (TODO).
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Lambda defaults when added: Python 3.12, ARM64, explicit LogGroup with 60-day
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retention. No DynamoDB — this is an S3 + Athena analytics workload (see CLAUDE.md).
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Lambdas: Python 3.12, ARM64, explicit LogGroup with 60-day retention.
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No DynamoDB — this is an S3 + Athena analytics workload (see CLAUDE.md).
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"""
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import os
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from aws_cdk import (
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BundlingOptions,
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Duration,
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RemovalPolicy,
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Stack,
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)
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from aws_cdk import (
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aws_glue as glue,
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)
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from aws_cdk import (
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aws_iam as iam,
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)
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from aws_cdk import (
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aws_lambda as lambda_,
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)
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from aws_cdk import (
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aws_logs as logs,
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)
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from aws_cdk import (
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aws_s3 as s3,
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)
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from aws_cdk import (
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aws_s3_notifications as s3n,
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)
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from aws_cdk import (
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aws_secretsmanager as secretsmanager,
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)
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from constructs import Construct
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GLUE_DATABASE = "apm_wo_analysis"
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GLUE_TABLE = "apm_wo_snapshots"
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ANTHROPIC_SECRET = "apm-wo-analysis/anthropic-api-key"
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LAMBDAS_DIR = os.path.join(os.path.dirname(__file__), "..", "..", "lambdas")
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class PipelineStack(Stack):
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def __init__(self, scope: Construct, construct_id: str, **kwargs) -> None:
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@ -61,7 +84,87 @@ class PipelineStack(Stack):
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)
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)
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# Phase 2 — classifier Lambda, S3-triggered on the raw/ prefix. TODO
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# Phase 3 — Glue database `apm_wo_analysis` + Athena workgroup
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# (partition projection on dt; no crawler). TODO
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# Phase 4 — slack-post Lambda + scoped IAM. TODO
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# Phase 2/3 — Glue database for the analytics dataset. The classifier
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# registers the apm_wo_snapshots table/partitions into it via awswrangler;
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# Phase 3 swaps in the partition-projection table definition + Athena.
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glue.CfnDatabase(
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self,
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"AnalyticsDb",
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catalog_id=self.account,
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database_input=glue.CfnDatabase.DatabaseInputProperty(name=GLUE_DATABASE),
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)
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# Phase 2 — classifier Lambda, S3-triggered on the raw/ prefix.
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# Deps (awswrangler/pandas/pyarrow/openpyxl) are Docker-bundled for ARM64
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# from lambdas/classifier/requirements.txt; boto3 ships in the runtime.
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classifier_logs = logs.LogGroup(
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self,
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"ClassifierLogs",
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log_group_name="/aws/lambda/apm-wo-analysis-classifier",
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retention=logs.RetentionDays.TWO_MONTHS,
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removal_policy=RemovalPolicy.DESTROY,
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)
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self.classifier_fn = lambda_.Function(
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self,
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"Classifier",
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function_name="apm-wo-analysis-classifier",
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runtime=lambda_.Runtime.PYTHON_3_12,
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architecture=lambda_.Architecture.ARM_64,
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handler="handler.handler",
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memory_size=512,
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timeout=Duration.seconds(120),
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log_group=classifier_logs,
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environment={"APM_HAIKU_FALLBACK": "on"},
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code=lambda_.Code.from_asset(
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os.path.join(LAMBDAS_DIR, "classifier"),
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bundling=BundlingOptions(
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image=lambda_.Runtime.PYTHON_3_12.bundling_image,
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platform="linux/arm64",
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command=[
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"bash",
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"-c",
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"pip install -r requirements.txt -t /asset-output "
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"&& cp -au . /asset-output",
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],
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),
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),
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)
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# S3 trigger: any .xlsx/.csv landing under raw/ invokes the classifier.
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for suffix in (".xlsx", ".csv"):
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self.exports_bucket.add_event_notification(
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s3.EventType.OBJECT_CREATED,
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s3n.LambdaDestination(self.classifier_fn),
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s3.NotificationKeyFilter(prefix="raw/", suffix=suffix),
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)
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# IAM — least privilege: read raw/, read+write analytics/, catalog the
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# snapshots table, and read the Anthropic key for the Haiku fallback.
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self.exports_bucket.grant_read(self.classifier_fn, "raw/*")
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self.exports_bucket.grant_read_write(self.classifier_fn, "analytics/*")
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self.classifier_fn.add_to_role_policy(
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iam.PolicyStatement(
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sid="GlueCatalogSnapshots",
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actions=[
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"glue:GetDatabase",
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"glue:GetTable",
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"glue:CreateTable",
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"glue:UpdateTable",
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"glue:GetPartition",
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"glue:GetPartitions",
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"glue:CreatePartition",
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"glue:BatchCreatePartition",
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"glue:UpdatePartition",
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],
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resources=[
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f"arn:aws:glue:{self.region}:{self.account}:catalog",
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f"arn:aws:glue:{self.region}:{self.account}:database/{GLUE_DATABASE}",
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f"arn:aws:glue:{self.region}:{self.account}:table/{GLUE_DATABASE}/*",
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],
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)
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)
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secretsmanager.Secret.from_secret_name_v2(
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self, "AnthropicKey", ANTHROPIC_SECRET
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).grant_read(self.classifier_fn)
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# Phase 4 — slack-post Lambda + scoped IAM; classifier async-invokes it. TODO
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@ -14,10 +14,18 @@ The authoritative spec is CLAUDE.md ("The classification model"). This module
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is owned by the classifier-engineer agent; the rule ladder and Haiku fallback
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are implemented in Phase 2 (docs/BUILD.md) and smoke-tested against a real
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export before merge.
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``classify()`` is fully deterministic: no network, no AWS, no Secrets access.
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The Haiku fallback lives in ``classify_with_haiku()`` and is never reached from
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inside ``classify()``.
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"""
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from __future__ import annotations
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import html
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import os
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import re
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# Axis 2 — Hold Reason → category.
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HOLD_TO_CATEGORY = {
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"SCHEDULING": "Awaiting Scheduling",
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@ -56,15 +64,286 @@ ACTION_NEEDED_CATEGORIES = ESCALATION_CATEGORIES | frozenset(
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}
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)
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# ---------------------------------------------------------------------------
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# Axis 1 — HTML stripping
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# ---------------------------------------------------------------------------
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_TAG_RE = re.compile(r"<[^>]+>")
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_URL_RE = re.compile(r"https?://\S+", re.IGNORECASE)
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_WS_RE = re.compile(r"\s+")
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# Smart quotes / dashes → ASCII so the rule ladder can use plain patterns.
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_SMART_MAP = {
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"‘": "'", # left single quote
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"’": "'", # right single quote
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"‚": "'", # single low-9 quote
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"‛": "'", # single high-reversed-9 quote
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"“": '"', # left double quote
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"”": '"', # right double quote
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"„": '"', # double low-9 quote
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"«": '"', # left guillemet
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"»": '"', # right guillemet
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"–": "-", # en dash
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"—": "-", # em dash
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"…": "...", # ellipsis
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" ": " ", # non-breaking space
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}
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_SMART_RE = re.compile("|".join(map(re.escape, _SMART_MAP)))
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def strip_html(comment: str | None) -> str:
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"""Unwrap the HTML-wrapped Last Comment and decode entities. (Phase 2)"""
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raise NotImplementedError
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"""Unwrap the HTML-wrapped Last Comment into clean plain text.
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Drops the ``<html>...</html>`` wrapper and all nested tags, decodes HTML
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entities, normalises smart quotes/dashes to ASCII, reduces embedded URLs to
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a readable ``link`` token, and collapses whitespace. ``None``/blank → "".
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"""
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if comment is None:
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return ""
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text = str(comment)
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# Decode entities first so e.g. <div> becomes a real tag we can strip,
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# then strip tags, then decode again for entities that were *inside* text.
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text = html.unescape(text)
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text = _TAG_RE.sub(" ", text)
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text = html.unescape(text)
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# Reduce URLs to a readable token (the raw link is noise for matching).
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text = _URL_RE.sub(" link ", text)
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text = _SMART_RE.sub(lambda m: _SMART_MAP[m.group(0)], text)
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text = _WS_RE.sub(" ", text).strip()
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return text
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# ---------------------------------------------------------------------------
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# Axis 1 — comment intent rule ladder (most-specific first)
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# ---------------------------------------------------------------------------
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def comment_intent(comment: str) -> str | None:
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"""Ordered, most-specific-first rule ladder; returns a bucket or None. (Phase 2)"""
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raise NotImplementedError
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"""Ordered, most-specific-first rule ladder over the *stripped* text.
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Returns one of the Axis-1 buckets in CLAUDE.md, or ``None`` when no rule
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fires. The caller is responsible for stripping HTML first; this operates on
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plain text. Ordering matters: the most specific / highest-severity intents
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are tested before generic ones so e.g. a 3rd-attempt escalation never falls
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through to a generic "report needed" rule.
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"""
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if not comment:
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return None
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t = comment
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low = t.lower()
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# -- Escalations: APM "Nth attempt process" cadence + explicit escalations.
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# Most-specific first: 3rd → 2nd → 1st. "Email/phone escalation sent" is the
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# escalation tell; the "attempt" number sets the level. A bare 1st-attempt
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# schedule-confirmation request is routine outreach, not an escalation, so
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# require an escalation/attempt signal beyond the plain "1st attempt".
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if re.search(r"\b3rd\b.*\battempt\b", low) or "3rd escalation" in low:
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return "3rd Escalation"
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if re.search(r"\b2nd\b.*\battempt\b", low) or "2nd escalation" in low:
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return "2nd Escalation"
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if "1st escalation" in low:
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return "1st Escalation"
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# -- SIM Ticket: Amazon SIM tooling references.
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if (
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re.search(r"\bsim\b.*\b(ticket|tt|v\d{6,})\b", low)
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or "t.corp.amazon.com" in low
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or re.search(r"\bsim\s*tt\b", low)
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):
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return "SIM Ticket"
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# -- Vendor No-Show: vendor failed to appear for a scheduled visit.
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if (
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"no show" in low
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or "no-show" in low
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or "noshow" in low
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or "did not show up" in low
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or "didn't show up" in low
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or "did not show" in low
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or "failed to show" in low
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):
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return "Vendor No-Show"
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# -- Cancelled: explicit cancellation language in the comment.
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if re.search(r"\bwo cancelled\b", low) or re.search(
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r"\bcancel(l)?ed\b.*\b(error|deprecat)", low
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):
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return "Cancelled"
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# -- Weekly WO Scheduled: the recurring-weekly cadence template.
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if re.search(r"\bweekly\s+wo\s+scheduled\b", low) or (
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"weekly" in low and "service reports are required weekly" in low
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):
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return "Weekly WO Scheduled"
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# -- Avetta Project Created: an Avetta project/work-request was opened.
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if re.search(r"\bavetta\s+project\s+created\b", low) or re.search(
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r"\bwork request\b.*\bcreated in avetta\b", low
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):
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return "Avetta Project Created"
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# -- Schedule Confirmed: vendor/site has a confirmed (re)schedule.
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if (
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"schedule confirmed with vendor" in low
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or "scheduled confirmed with vendor" in low
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or re.search(r"\bschedule is confirmed\b", low)
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or re.search(r"\b(re)?scheduling confirmed\b", low)
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or re.search(r"\bconfirmed scheduled date\b", low)
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or re.search(r"\bconfirmed schedule(d)? (date|for)\b", low)
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or re.search(r"\bvendor confirmed\b", low)
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or re.search(r"^confirmed\b", low)
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):
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return "Schedule Confirmed"
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# -- Completed / Pending Close: work done but WO not yet closed.
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if (
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re.search(r"\bperformed task\b", low)
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or re.search(r"\bcompleted by\b", low)
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or re.search(r"\bcan'?t close\b", low) # cant/can't close
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or re.search(r"\bcannot close\b", low)
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or re.search(r"\bunable to close\b", low)
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or "will not allow me to close" in low
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or "will not let me close" in low
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or re.search(r"\bpending close\b", low)
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):
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return "Completed / Pending Close"
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# -- Rescheduled: an explicit reschedule/new-date event (no confirmation).
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if (
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re.search(r"\brescheduled\b", low)
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or re.search(r"\bre-?scheduling\b", low)
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or re.search(r"\bnew eta\b", low)
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or re.search(r"\btentative(ly)? schedule", low)
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):
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return "Rescheduled"
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# -- Report / Docs Needed: completion/service report or docs are requested.
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if (
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"completion report" in low
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or "service report" in low
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or "completion validation" in low
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or re.search(r"\bservice reports? (are )?(required|needed)\b", low)
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or re.search(r"\bpm service report\b", low)
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or re.search(r"\b(upload|provide).{0,30}\b(report|document|docs|jha)\b", low)
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or re.search(r"\bclosing documents\b", low)
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or re.search(r"\bcompletion document", low)
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):
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return "Report / Docs Needed"
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# -- Awaiting Report / Invoice: waiting on a report or invoice to land.
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if (
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re.search(r"\bpending reports?\b", low)
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or re.search(r"\bawaiting (the )?(report|invoice)\b", low)
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or re.search(r"\binvoice from\b", low)
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or re.search(r"\bawaiting invoice\b", low)
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):
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return "Awaiting Report / Invoice"
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# -- Awaiting Scheduling: asking the team/vendor to schedule or confirm.
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if (
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re.search(r"\bplease s?c?hedule\b", low) # "please schedule"/"shedule" typo
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or re.search(r"\bplease provide schedul", low)
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or re.search(r"\bplease advise on schedul", low)
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or re.search(r"\bprovide update on schedul", low)
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or re.search(r"\bneeds? (a )?confirmed schedul", low)
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or re.search(r"\bget (this )?scheduled\b", low)
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or re.search(r"\bschedule confirmation\b", low)
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or re.search(r"\bupdates? on scheduling\b", low)
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or re.search(r"\bget this scheduled\b", low)
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or re.search(r"\bwaiting for a schedule", low)
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or re.search(r"\bawaiting (a )?schedul", low)
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or re.search(r"\b1\s*st attempt process for schedule confirmation\b", low)
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or re.search(r"\brequesting service date\b", low)
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or re.search(r"\bneed(s|ed)? to (be )?(re)?schedul", low)
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or re.search(r"\bcan we (please )?get this scheduled\b", low)
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):
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return "Awaiting Scheduling"
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# -- Status Inquiry: a question about status/ETA/updates.
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if (
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re.search(r"\bany update", low)
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or re.search(r"\bupdates?\?", low)
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or re.search(r"\beta\b", low) # any mention of an ETA is a status inquiry
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or re.search(r"\bdo you have an eta\b", low)
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or (low.endswith("?") and ("update" in low or "eta" in low))
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or re.search(r"\bplease confirm whether\b", low)
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or re.search(r"\bconfirm completion or scheduling status\b", low)
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):
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return "Status Inquiry"
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# -- On Hold: explicit on-hold language in the comment.
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if re.search(r"\bon[- ]?hold\b", low) or re.search(r"\bwork order on hold\b", low):
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return "On Hold"
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# -- Acknowledgement / No-op: terse acks ("Copy", "Copy 5/26.").
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if re.fullmatch(r"copy[.\s\d/]*", low) or re.fullmatch(
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r"(noted|ack(nowledged)?)[.\s]*", low
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):
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return "Acknowledgement / No-op"
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return None
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# ---------------------------------------------------------------------------
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# Resolution policy + mismatch detection
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# ---------------------------------------------------------------------------
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# Comment intents that assert the work is essentially done / scheduled.
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_DONE_INTENTS = frozenset(
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{
|
||||
"Completed / Pending Close",
|
||||
"Schedule Confirmed",
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def _hold_category(hold_reason: str | None) -> str | None:
|
||||
if not hold_reason:
|
||||
return None
|
||||
return HOLD_TO_CATEGORY.get(hold_reason.strip().upper())
|
||||
|
||||
|
||||
def _mismatch(
|
||||
intent: str | None,
|
||||
wo_status: str | None,
|
||||
hold_reason: str | None,
|
||||
) -> str | None:
|
||||
"""Flag when comment intent contradicts the structured state.
|
||||
|
||||
A mismatch is a feature, not noise: it surfaces WOs whose free-text claims
|
||||
progress the structured state has not caught up with. The returned string is
|
||||
human-readable and goes straight onto the snapshot row.
|
||||
"""
|
||||
if intent is None:
|
||||
return None
|
||||
status = (wo_status or "").strip().upper()
|
||||
hold = (hold_reason or "").strip().upper()
|
||||
|
||||
# Comment claims completion/confirmation while still on a blocking hold.
|
||||
if intent in _DONE_INTENTS and hold in {
|
||||
"REPORT",
|
||||
"SCHEDULING",
|
||||
"VENDOR",
|
||||
"PARTS",
|
||||
"ORDER",
|
||||
}:
|
||||
verb = (
|
||||
"completed"
|
||||
if intent == "Completed / Pending Close"
|
||||
else "schedule confirmed"
|
||||
)
|
||||
return f"comment says {verb} but WO is on a {hold} hold"
|
||||
|
||||
# Comment claims completion while the WO Status is still in-flight.
|
||||
if intent == "Completed / Pending Close" and status in WO_STATUS_IN_FLIGHT:
|
||||
return f"comment says completed but WO Status is {status}"
|
||||
|
||||
# Comment claims a vendor no-show while the WO carries a schedule-confirmed
|
||||
# type hold — the two stories disagree.
|
||||
if intent == "Vendor No-Show" and hold == "SCHEDULING":
|
||||
return "comment reports vendor no-show but WO is on a SCHEDULING hold"
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def classify(
|
||||
|
|
@ -72,5 +351,201 @@ def classify(
|
|||
hold_reason: str | None,
|
||||
last_comment: str | None,
|
||||
) -> tuple[str, str | None]:
|
||||
"""Resolve to ``(final_category, mismatch_reason | None)``. (Phase 2)"""
|
||||
raise NotImplementedError
|
||||
"""Resolve to ``(final_category, mismatch_reason | None)``.
|
||||
|
||||
Fully deterministic — no network, no AWS. Resolution policy:
|
||||
|
||||
1. Comment intent wins when confident (the ladder fired).
|
||||
2. Else structured state: ``Hold Reason`` map, then ``WO Status``
|
||||
(``RCAN`` → Cancelled, ``H`` corroborates On Hold).
|
||||
3. Else ``Other``.
|
||||
|
||||
Regardless of which axis decides the category, a mismatch reason is computed
|
||||
whenever the comment intent contradicts the structured state and surfaced on
|
||||
the result (never suppressed).
|
||||
"""
|
||||
status = (wo_status or "").strip().upper()
|
||||
text = strip_html(last_comment)
|
||||
intent = comment_intent(text)
|
||||
mismatch = _mismatch(intent, status, hold_reason)
|
||||
|
||||
# 1. Comment intent wins when confident.
|
||||
if intent is not None:
|
||||
return intent, mismatch
|
||||
|
||||
# 2. Structured state.
|
||||
hold_cat = _hold_category(hold_reason)
|
||||
if hold_cat is not None:
|
||||
return hold_cat, mismatch
|
||||
if status == WO_STATUS_CANCELLED:
|
||||
return "Cancelled", mismatch
|
||||
if status == WO_STATUS_HOLD:
|
||||
return "On Hold", mismatch
|
||||
|
||||
# 3. Nothing matched.
|
||||
return "Other", mismatch
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Haiku fallback — isolated, lazy, network-only, NEVER reached from classify()
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
_HAIKU_MODEL = "claude-haiku-4-5-20251001"
|
||||
_SECRET_NAME = "apm-wo-analysis/anthropic-api-key"
|
||||
_ANTHROPIC_URL = "https://api.anthropic.com/v1/messages"
|
||||
_ANTHROPIC_VERSION = "2023-06-01"
|
||||
|
||||
# Buckets Haiku is allowed to choose from (Axis-1 taxonomy).
|
||||
_HAIKU_BUCKETS = (
|
||||
"1st Escalation",
|
||||
"2nd Escalation",
|
||||
"3rd Escalation",
|
||||
"SIM Ticket",
|
||||
"Vendor No-Show",
|
||||
"Weekly WO Scheduled",
|
||||
"Schedule Confirmed",
|
||||
"Awaiting Scheduling",
|
||||
"Report / Docs Needed",
|
||||
"Awaiting Report / Invoice",
|
||||
"Completed / Pending Close",
|
||||
"Acknowledgement / No-op",
|
||||
"Cancelled",
|
||||
"On Hold",
|
||||
"Rescheduled",
|
||||
"Avetta Project Created",
|
||||
"Other Escalation",
|
||||
"Status Inquiry",
|
||||
"Other",
|
||||
)
|
||||
|
||||
|
||||
def _haiku_enabled() -> bool:
|
||||
"""The fallback is gated behind ``APM_HAIKU_FALLBACK`` (default enabled)."""
|
||||
return os.environ.get("APM_HAIKU_FALLBACK", "1").strip().lower() not in {
|
||||
"0",
|
||||
"false",
|
||||
"no",
|
||||
"off",
|
||||
"",
|
||||
}
|
||||
|
||||
|
||||
def _is_trivial(text: str) -> bool:
|
||||
"""Too short / contentless to be worth an LLM call."""
|
||||
return len(text) < 4
|
||||
|
||||
|
||||
def _fetch_api_key() -> str:
|
||||
"""Read the Anthropic key from Secrets Manager (lazy, call-time only)."""
|
||||
import json
|
||||
|
||||
import boto3 # imported lazily so classify()/tests never need boto3
|
||||
|
||||
client = boto3.client("secretsmanager")
|
||||
resp = client.get_secret_value(SecretId=_SECRET_NAME)
|
||||
raw = resp.get("SecretString") or ""
|
||||
raw = raw.strip()
|
||||
# Support both a bare string secret and a JSON {"anthropic-api-key": "..."}.
|
||||
if raw.startswith("{"):
|
||||
data = json.loads(raw)
|
||||
for key in ("anthropic-api-key", "ANTHROPIC_API_KEY", "api_key", "key"):
|
||||
if key in data:
|
||||
return data[key]
|
||||
# Single-value JSON object: take the only value.
|
||||
if len(data) == 1:
|
||||
return next(iter(data.values()))
|
||||
raise KeyError("anthropic api key not found in secret JSON")
|
||||
return raw
|
||||
|
||||
|
||||
def _call_haiku(text: str, api_key: str) -> str | None:
|
||||
"""POST to the Anthropic messages API via stdlib urllib; return a bucket."""
|
||||
import json
|
||||
import urllib.error
|
||||
import urllib.request
|
||||
|
||||
bucket_list = "\n".join(f"- {b}" for b in _HAIKU_BUCKETS)
|
||||
prompt = (
|
||||
"You classify a maintenance work-order's last comment into exactly one "
|
||||
"category. The deterministic rules failed and there is no structured "
|
||||
"hold signal, so judge the free text alone. Reply with ONLY the exact "
|
||||
"category name from this list, nothing else:\n"
|
||||
f"{bucket_list}\n\n"
|
||||
f"Comment:\n{text}"
|
||||
)
|
||||
body = json.dumps(
|
||||
{
|
||||
"model": _HAIKU_MODEL,
|
||||
"max_tokens": 20,
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
}
|
||||
).encode("utf-8")
|
||||
req = urllib.request.Request(
|
||||
_ANTHROPIC_URL,
|
||||
data=body,
|
||||
headers={
|
||||
"content-type": "application/json",
|
||||
"x-api-key": api_key,
|
||||
"anthropic-version": _ANTHROPIC_VERSION,
|
||||
},
|
||||
method="POST",
|
||||
)
|
||||
try:
|
||||
with urllib.request.urlopen(req, timeout=15) as resp:
|
||||
payload = json.loads(resp.read().decode("utf-8"))
|
||||
except (urllib.error.URLError, TimeoutError, ValueError):
|
||||
return None
|
||||
|
||||
parts = payload.get("content") or []
|
||||
raw = "".join(p.get("text", "") for p in parts if isinstance(p, dict)).strip()
|
||||
if not raw:
|
||||
return None
|
||||
# Normalise: pick the listed bucket that matches the model's reply.
|
||||
for bucket in _HAIKU_BUCKETS:
|
||||
if bucket.lower() == raw.lower():
|
||||
return bucket
|
||||
for bucket in _HAIKU_BUCKETS:
|
||||
if bucket.lower() in raw.lower():
|
||||
return bucket
|
||||
return None
|
||||
|
||||
|
||||
def classify_with_haiku(
|
||||
wo_status: str | None,
|
||||
hold_reason: str | None,
|
||||
last_comment: str | None,
|
||||
) -> tuple[str, str | None]:
|
||||
"""Deterministic ``classify()`` first; Haiku only for true residual ``Other``.
|
||||
|
||||
The LLM is consulted strictly when ALL hold:
|
||||
* deterministic result is ``Other``;
|
||||
* ``Hold Reason`` is blank (no structured signal to fall back on);
|
||||
* the stripped comment is non-trivial;
|
||||
* ``APM_HAIKU_FALLBACK`` is enabled.
|
||||
|
||||
Everything network-related (boto3, the Anthropic call) is imported and
|
||||
invoked lazily here, so ``classify()`` and the test-suite never touch it.
|
||||
The mismatch reason from the deterministic pass is preserved.
|
||||
"""
|
||||
category, mismatch = classify(wo_status, hold_reason, last_comment)
|
||||
|
||||
if category != "Other":
|
||||
return category, mismatch
|
||||
if (hold_reason or "").strip():
|
||||
return category, mismatch
|
||||
if not _haiku_enabled():
|
||||
return category, mismatch
|
||||
|
||||
text = strip_html(last_comment)
|
||||
if _is_trivial(text):
|
||||
return category, mismatch
|
||||
|
||||
try:
|
||||
api_key = _fetch_api_key()
|
||||
guess = _call_haiku(text, api_key)
|
||||
except Exception:
|
||||
return category, mismatch
|
||||
|
||||
if guess and guess != "Other":
|
||||
return guess, mismatch
|
||||
return category, mismatch
|
||||
|
|
|
|||
|
|
@ -1,19 +1,232 @@
|
|||
"""S3-triggered classifier Lambda: parse export → two-axis classify → Parquet.
|
||||
"""S3-triggered classifier Lambda: parse export -> two-axis classify -> Parquet.
|
||||
|
||||
Triggered on ``s3:ObjectCreated`` under the ``raw/`` prefix regardless of how
|
||||
the file arrives (direct upload or local drop-folder). For each non-blank-comment
|
||||
row it strips HTML, runs ``classify()``, derives ``is_escalation`` / ``is_action``
|
||||
/ ``mismatch``, writes a per-WO snapshot to ``analytics/dt=YYYY-MM-DD/`` as
|
||||
Parquet (registering the Glue partition), emits a small ``summary.json`` for the
|
||||
slack-post Lambda, then invokes it.
|
||||
Triggered on ``s3:ObjectCreated`` under the ``raw/`` prefix regardless of how the
|
||||
file arrives (direct upload or local drop-folder). For each non-blank-comment row
|
||||
it strips HTML, runs the two-axis classifier, derives ``is_escalation`` /
|
||||
``is_action`` / ``mismatch``, writes a per-WO snapshot to ``analytics/dt=YYYY-MM-DD/``
|
||||
as Parquet (registering the Glue partition), and emits a small ``summary.json``
|
||||
for the slack-post Lambda to read cheaply.
|
||||
|
||||
Runtime when wired up: Python 3.12, ARM64, 512 MB, 120 s, awswrangler layer.
|
||||
Implemented in Phase 2 (docs/BUILD.md).
|
||||
The slack-post invocation is wired in Phase 4 (the function does not exist yet).
|
||||
Runtime: Python 3.12, ARM64, 512 MB, 120 s. pandas/pyarrow/awswrangler are
|
||||
bundled from requirements.txt; boto3 is provided by the Lambda runtime.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import csv
|
||||
import json
|
||||
import os
|
||||
import tempfile
|
||||
from collections import Counter
|
||||
from datetime import datetime, timezone
|
||||
from urllib.parse import unquote_plus
|
||||
|
||||
import awswrangler as wr
|
||||
import boto3
|
||||
import openpyxl
|
||||
import pandas as pd
|
||||
|
||||
import classify as clf
|
||||
|
||||
GLUE_DATABASE = "apm_wo_analysis"
|
||||
GLUE_TABLE = "apm_wo_snapshots"
|
||||
ANALYTICS_PREFIX = "analytics"
|
||||
|
||||
# Map snapshot field -> substring matched (case-insensitively) against the export
|
||||
# header, tolerating minor header drift in the 13-column APM export.
|
||||
COLUMN_MATCHERS = {
|
||||
"wo_number": "wo number",
|
||||
"wo_description": "wo description",
|
||||
"equipment_code": "equipment",
|
||||
"site": "organization",
|
||||
"due_date": "due date",
|
||||
"department": "department",
|
||||
"wo_status": "wo status",
|
||||
"hold_reason": "hold reason",
|
||||
"last_comment": "last comment",
|
||||
"last_comment_by": "last comment by",
|
||||
"last_comment_date": "last comment date",
|
||||
"contractor": "contractor",
|
||||
"contractor_description": "contractor description",
|
||||
}
|
||||
|
||||
_s3 = boto3.client("s3")
|
||||
|
||||
|
||||
def _haiku_enabled() -> bool:
|
||||
return os.environ.get("APM_HAIKU_FALLBACK", "on").strip().lower() in (
|
||||
"1",
|
||||
"on",
|
||||
"true",
|
||||
"yes",
|
||||
)
|
||||
|
||||
|
||||
def _resolve_classifier():
|
||||
"""Use the Haiku-fallback classifier when enabled, else the deterministic one."""
|
||||
if _haiku_enabled() and hasattr(clf, "classify_with_haiku"):
|
||||
return clf.classify_with_haiku
|
||||
return clf.classify
|
||||
|
||||
|
||||
def _norm(value) -> str:
|
||||
if value is None:
|
||||
return ""
|
||||
if isinstance(value, datetime):
|
||||
return value.isoformat()
|
||||
return str(value).strip()
|
||||
|
||||
|
||||
def _resolve_columns(header: list[str]) -> dict[str, int]:
|
||||
"""Map each snapshot field to its column index in the export header."""
|
||||
lowered = [(_norm(h).lower(), i) for i, h in enumerate(header)]
|
||||
resolved: dict[str, int] = {}
|
||||
for field, needle in COLUMN_MATCHERS.items():
|
||||
# "last comment" is a prefix of "last comment by"/"date"; prefer exact-ish.
|
||||
exact = [i for h, i in lowered if h == needle]
|
||||
contains = [i for h, i in lowered if needle in h]
|
||||
match = exact or contains
|
||||
if match:
|
||||
resolved[field] = match[0]
|
||||
return resolved
|
||||
|
||||
|
||||
def _read_rows(path: str, key: str) -> tuple[list[str], list[list]]:
|
||||
"""Return (header, data_rows) from an xlsx or csv export."""
|
||||
if key.lower().endswith(".csv"):
|
||||
with open(path, newline="", encoding="utf-8-sig") as fh:
|
||||
reader = list(csv.reader(fh))
|
||||
return reader[0], reader[1:]
|
||||
wb = openpyxl.load_workbook(path, read_only=True, data_only=True)
|
||||
ws = wb.active
|
||||
rows = list(ws.iter_rows(values_only=True))
|
||||
wb.close()
|
||||
return list(rows[0]), [list(r) for r in rows[1:]]
|
||||
|
||||
|
||||
def _build_snapshot(header: list[str], data: list[list]) -> tuple[pd.DataFrame, int]:
|
||||
"""Classify each non-blank-comment row into a snapshot DataFrame."""
|
||||
cols = _resolve_columns(header)
|
||||
if "last_comment" not in cols or "wo_status" not in cols:
|
||||
raise ValueError(f"Export missing required columns; resolved={list(cols)}")
|
||||
|
||||
classifier = _resolve_classifier()
|
||||
records: list[dict] = []
|
||||
blank = 0
|
||||
for row in data:
|
||||
|
||||
def cell(field: str):
|
||||
idx = cols.get(field)
|
||||
return row[idx] if idx is not None and idx < len(row) else None
|
||||
|
||||
raw_comment = cell("last_comment")
|
||||
comment = clf.strip_html(raw_comment)
|
||||
if not comment:
|
||||
blank += 1 # blank-comment rows excluded from the classified total
|
||||
continue
|
||||
|
||||
status = _norm(cell("wo_status"))
|
||||
hold = _norm(cell("hold_reason"))
|
||||
category, mismatch = classifier(status, hold, comment)
|
||||
|
||||
records.append(
|
||||
{
|
||||
"wo_number": _norm(cell("wo_number")),
|
||||
"wo_description": _norm(cell("wo_description")),
|
||||
"equipment_code": _norm(cell("equipment_code")),
|
||||
"site": _norm(cell("site")),
|
||||
"due_date": _norm(cell("due_date")),
|
||||
"department": _norm(cell("department")),
|
||||
"wo_status": status,
|
||||
"hold_reason": hold,
|
||||
"last_comment": comment,
|
||||
"last_comment_by": _norm(cell("last_comment_by")),
|
||||
"last_comment_date": _norm(cell("last_comment_date")),
|
||||
"contractor": _norm(cell("contractor")),
|
||||
"contractor_description": _norm(cell("contractor_description")),
|
||||
"category": category,
|
||||
"is_escalation": category in clf.ESCALATION_CATEGORIES,
|
||||
"is_action": category in clf.ACTION_NEEDED_CATEGORIES,
|
||||
"mismatch": mismatch or "",
|
||||
}
|
||||
)
|
||||
return pd.DataFrame.from_records(records), blank
|
||||
|
||||
|
||||
def _build_summary(df: pd.DataFrame, dt: str, key: str, blank: int) -> dict:
|
||||
category_counts = Counter(df["category"])
|
||||
site_counts = Counter(s for s in df["site"] if s)
|
||||
mismatches = [
|
||||
{"wo_number": r.wo_number, "category": r.category, "mismatch": r.mismatch}
|
||||
for r in df.itertuples()
|
||||
if r.mismatch
|
||||
]
|
||||
return {
|
||||
"dt": dt,
|
||||
"source_key": key,
|
||||
"classified_total": int(len(df)),
|
||||
"blank_comment_rows": int(blank),
|
||||
"category_counts": dict(category_counts),
|
||||
"escalation_total": int(df["is_escalation"].sum()),
|
||||
"third_escalation_count": int(category_counts.get("3rd Escalation", 0)),
|
||||
"action_needed": int(df["is_action"].sum()),
|
||||
"routine": int((~df["is_action"]).sum()),
|
||||
"top_sites": [{"site": s, "count": n} for s, n in site_counts.most_common(10)],
|
||||
"mismatches": mismatches,
|
||||
"generated_at": datetime.now(timezone.utc).isoformat(),
|
||||
}
|
||||
|
||||
|
||||
def handler(event, context):
|
||||
"""Lambda entry point. (Phase 2)"""
|
||||
raise NotImplementedError
|
||||
"""Classify each export dropped under raw/ into a daily Parquet snapshot."""
|
||||
record = event["Records"][0]
|
||||
bucket = record["s3"]["bucket"]["name"]
|
||||
key = unquote_plus(record["s3"]["object"]["key"])
|
||||
|
||||
if not key.startswith("raw/") or not key.lower().endswith((".xlsx", ".csv")):
|
||||
print(f"Skipping non-export object: s3://{bucket}/{key}")
|
||||
return {"skipped": key}
|
||||
|
||||
dt = datetime.now(timezone.utc).strftime("%Y-%m-%d")
|
||||
print(f"Classifying s3://{bucket}/{key} into dt={dt}")
|
||||
|
||||
with tempfile.NamedTemporaryFile(suffix=os.path.splitext(key)[1]) as tmp:
|
||||
_s3.download_fileobj(bucket, key, tmp)
|
||||
tmp.flush()
|
||||
header, data = _read_rows(tmp.name, key)
|
||||
|
||||
df, blank = _build_snapshot(header, data)
|
||||
if df.empty:
|
||||
print("No classifiable rows (all comments blank); nothing written.")
|
||||
return {"classified": 0, "blank": blank}
|
||||
|
||||
df["dt"] = dt
|
||||
wr.s3.to_parquet(
|
||||
df=df,
|
||||
path=f"s3://{bucket}/{ANALYTICS_PREFIX}/",
|
||||
dataset=True,
|
||||
partition_cols=["dt"],
|
||||
mode="overwrite_partitions",
|
||||
database=GLUE_DATABASE,
|
||||
table=GLUE_TABLE,
|
||||
)
|
||||
|
||||
summary = _build_summary(df, dt, key, blank)
|
||||
_s3.put_object(
|
||||
Bucket=bucket,
|
||||
Key=f"{ANALYTICS_PREFIX}/dt={dt}/summary.json",
|
||||
Body=json.dumps(summary, indent=2).encode("utf-8"),
|
||||
ContentType="application/json",
|
||||
)
|
||||
|
||||
# Phase 4: async-invoke the slack-post Lambda here once it exists.
|
||||
print(
|
||||
f"Wrote {len(df)} rows, {summary['escalation_total']} escalations "
|
||||
f"({summary['third_escalation_count']} 3rd), {len(summary['mismatches'])} mismatches."
|
||||
)
|
||||
return {
|
||||
"classified": int(len(df)),
|
||||
"dt": dt,
|
||||
"summary_key": f"dt={dt}/summary.json",
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,24 +1,246 @@
|
|||
"""Smoke test for the two-axis classifier.
|
||||
|
||||
The full smoke test — run ``classify()`` over the sample export
|
||||
(~/Downloads/_documents/Sheet1-1.xlsx) and assert "Other" <= ~10% with known
|
||||
fixtures landing in the right buckets — is added in Phase 2 (docs/BUILD.md) and
|
||||
owned by the classifier-engineer agent. This placeholder keeps the test layout
|
||||
in place and guards the classification constants.
|
||||
Runs the DETERMINISTIC ``classify()`` (no Haiku, no network, no AWS) over the
|
||||
canonical sample export and asserts the two-axis model keeps "Other" in the
|
||||
single digits, plus a handful of known fixtures land in the right bucket. The
|
||||
Haiku fallback is never exercised here.
|
||||
|
||||
Canonical fixture: ``~/Downloads/_documents/Sheet1-1.xlsx`` (347 rows, 13 cols).
|
||||
If the file is absent the export-driven tests skip. Run with the repo venv:
|
||||
|
||||
./.venv/bin/python -m pytest tests/test_classify.py -s -q
|
||||
"""
|
||||
|
||||
import collections
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
sys.path.insert(
|
||||
0, str(Path(__file__).resolve().parent.parent / "lambdas" / "classifier")
|
||||
)
|
||||
|
||||
import classify # noqa: E402
|
||||
|
||||
# Column indices in the 13-column export.
|
||||
COL_WO_STATUS = 6
|
||||
COL_HOLD_REASON = 7
|
||||
COL_LAST_COMMENT = 8
|
||||
|
||||
FIXTURE = Path.home() / "Downloads" / "_documents" / "Sheet1-1.xlsx"
|
||||
|
||||
# Max acceptable deterministic "Other" share before any AI. CLAUDE.md: the
|
||||
# two-axis model lands ~9% before Haiku; we hold the line at single digits.
|
||||
MAX_OTHER_PCT = 10.0
|
||||
|
||||
|
||||
def test_classification_constants_well_formed():
|
||||
assert "3rd Escalation" in classify.ESCALATION_CATEGORIES
|
||||
assert classify.HOLD_TO_CATEGORY["SCHEDULING"] == "Awaiting Scheduling"
|
||||
# Every escalation category is also action-needed.
|
||||
assert classify.ESCALATION_CATEGORIES <= classify.ACTION_NEEDED_CATEGORIES
|
||||
|
||||
|
||||
def test_strip_html_unwraps_and_normalises():
|
||||
assert classify.strip_html(None) == ""
|
||||
assert classify.strip_html(" ") == ""
|
||||
assert classify.strip_html("<html>WO schedule confirmed with vendor</html>") == (
|
||||
"WO schedule confirmed with vendor"
|
||||
)
|
||||
# Nested tags, entities, smart quotes, and embedded URLs.
|
||||
raw = (
|
||||
"<html><div>1st attempt process for schedule confirmation. "
|
||||
"Vendor, please confirm ‘Schedule Start Date’ & proceed. "
|
||||
"https://app.avetta.com/avt-cli/x</div></html>"
|
||||
)
|
||||
cleaned = classify.strip_html(raw)
|
||||
assert "<" not in cleaned and ">" not in cleaned
|
||||
assert "‘" not in cleaned and "’" not in cleaned # smart quotes gone
|
||||
assert "'Schedule Start Date'" in cleaned
|
||||
assert "&" not in cleaned and "&" in cleaned # entity decoded
|
||||
assert "https://" not in cleaned # URL reduced to a token
|
||||
|
||||
|
||||
def test_known_intents():
|
||||
# Schedule confirmed (the dominant happy-path comment).
|
||||
cat, _ = classify.classify(
|
||||
"IP", "", "<html>WO schedule confirmed with vendor</html>"
|
||||
)
|
||||
assert cat == "Schedule Confirmed"
|
||||
|
||||
# Cancelled via WO Status, even with a generic comment.
|
||||
cat, _ = classify.classify(
|
||||
"RCAN", "", "<html>WO Cancelled, created in error.</html>"
|
||||
)
|
||||
assert cat == "Cancelled"
|
||||
|
||||
# 3rd-attempt escalation cadence.
|
||||
cat, _ = classify.classify(
|
||||
"H", "REPORT", "<html>3rd attempt process for schedule confirmation.</html>"
|
||||
)
|
||||
assert cat == "3rd Escalation"
|
||||
|
||||
# Vendor no-show.
|
||||
cat, _ = classify.classify(
|
||||
"R", "", "<html>Site tech reported vendor was a no show for Friday.</html>"
|
||||
)
|
||||
assert cat == "Vendor No-Show"
|
||||
|
||||
# Weekly cadence template.
|
||||
cat, _ = classify.classify(
|
||||
"IP",
|
||||
"",
|
||||
"<html>Weekly WO scheduled. Service reports required EOD Friday.</html>",
|
||||
)
|
||||
assert cat == "Weekly WO Scheduled"
|
||||
|
||||
# Structured-only fallback: no comment intent fires, REPORT hold decides.
|
||||
cat, _ = classify.classify("IP", "REPORT", "<html></html>")
|
||||
assert cat == "Report / Docs Needed"
|
||||
|
||||
|
||||
def test_mismatch_detection():
|
||||
# Comment claims completion while on a REPORT hold → mismatch surfaced.
|
||||
cat, mm = classify.classify(
|
||||
"IP", "REPORT", "<html>Vendor arrived and performed task.</html>"
|
||||
)
|
||||
assert cat == "Completed / Pending Close"
|
||||
assert mm is not None and "REPORT" in mm
|
||||
|
||||
# Schedule confirmed while on a SCHEDULING hold → mismatch surfaced.
|
||||
cat, mm = classify.classify(
|
||||
"R", "SCHEDULING", "<html>WO schedule confirmed with vendor.</html>"
|
||||
)
|
||||
assert cat == "Schedule Confirmed"
|
||||
assert mm is not None
|
||||
|
||||
# Clean case: no contradiction → no mismatch.
|
||||
_, mm = classify.classify(
|
||||
"IP", "", "<html>WO schedule confirmed with vendor</html>"
|
||||
)
|
||||
assert mm is None
|
||||
|
||||
|
||||
def test_classify_is_offline():
|
||||
"""classify() must not import boto3 or reach the network."""
|
||||
import sys as _sys
|
||||
|
||||
had_boto3 = "boto3" in _sys.modules
|
||||
classify.classify("IP", "REPORT", "<html>1st attempt process for report.</html>")
|
||||
# If boto3 wasn't already loaded, classify() must not have pulled it in.
|
||||
if not had_boto3:
|
||||
assert "boto3" not in _sys.modules
|
||||
|
||||
|
||||
@pytest.mark.skipif(not FIXTURE.exists(), reason=f"sample export not found: {FIXTURE}")
|
||||
def test_other_share_against_real_export(capsys):
|
||||
import openpyxl
|
||||
|
||||
wb = openpyxl.load_workbook(FIXTURE, read_only=True, data_only=True)
|
||||
rows = list(wb.active.iter_rows(values_only=True))[1:] # drop header
|
||||
|
||||
dist = collections.Counter()
|
||||
mismatches = 0
|
||||
classified = 0
|
||||
blank = 0
|
||||
other_samples = []
|
||||
|
||||
for row in rows:
|
||||
comment = row[COL_LAST_COMMENT]
|
||||
# Blank-comment rows are excluded from the classified total by design.
|
||||
if comment is None or str(comment).strip() == "":
|
||||
blank += 1
|
||||
continue
|
||||
classified += 1
|
||||
category, mismatch = classify.classify(
|
||||
row[COL_WO_STATUS], row[COL_HOLD_REASON], comment
|
||||
)
|
||||
dist[category] += 1
|
||||
if mismatch:
|
||||
mismatches += 1
|
||||
if category == "Other" and len(other_samples) < 20:
|
||||
other_samples.append(classify.strip_html(comment)[:90])
|
||||
|
||||
other = dist["Other"]
|
||||
other_pct = other * 100.0 / classified if classified else 0.0
|
||||
|
||||
with capsys.disabled():
|
||||
print(f"\n=== APM classifier smoke test: {FIXTURE.name} ===")
|
||||
print(
|
||||
f"rows={len(rows)} classified={classified} "
|
||||
f"blank-excluded={blank} (blank-comment rows excluded from the total)"
|
||||
)
|
||||
print("--- category distribution ---")
|
||||
for cat, count in dist.most_common():
|
||||
print(f" {count:4d} {count * 100.0 / classified:5.1f}% {cat}")
|
||||
print(f"--- Other: {other} ({other_pct:.2f}%) ---")
|
||||
for sample in other_samples:
|
||||
print(f" [Other] {sample}")
|
||||
print(f"--- mismatches flagged: {mismatches} ---")
|
||||
|
||||
assert classified > 0
|
||||
assert other_pct <= MAX_OTHER_PCT, (
|
||||
f"deterministic Other {other_pct:.2f}% exceeds {MAX_OTHER_PCT}% — "
|
||||
"the two-axis ladder regressed"
|
||||
)
|
||||
# Two-axis model should comfortably beat the legacy ~17% Other.
|
||||
assert other_pct < 17.0
|
||||
|
||||
|
||||
@pytest.mark.skipif(not FIXTURE.exists(), reason=f"sample export not found: {FIXTURE}")
|
||||
def test_known_fixture_rows_in_export():
|
||||
"""Anchor on real rows found in the canonical export."""
|
||||
import openpyxl
|
||||
|
||||
wb = openpyxl.load_workbook(FIXTURE, read_only=True, data_only=True)
|
||||
rows = list(wb.active.iter_rows(values_only=True))[1:]
|
||||
|
||||
by_status = collections.defaultdict(list)
|
||||
schedule_confirmed_row = None
|
||||
report_completion_mismatch = None
|
||||
|
||||
for row in rows:
|
||||
comment = row[COL_LAST_COMMENT]
|
||||
if comment is None or str(comment).strip() == "":
|
||||
continue
|
||||
text = classify.strip_html(comment)
|
||||
by_status[row[COL_WO_STATUS]].append(row)
|
||||
if (
|
||||
schedule_confirmed_row is None
|
||||
and "schedule confirmed with vendor" in text.lower()
|
||||
and not (row[COL_HOLD_REASON] or "").strip()
|
||||
):
|
||||
schedule_confirmed_row = row
|
||||
if (
|
||||
report_completion_mismatch is None
|
||||
and (row[COL_HOLD_REASON] or "").strip().upper() == "REPORT"
|
||||
and "performed task" in text.lower()
|
||||
):
|
||||
report_completion_mismatch = row
|
||||
|
||||
# A "WO schedule confirmed with vendor" row → Schedule Confirmed.
|
||||
assert schedule_confirmed_row is not None, "fixture lacks a schedule-confirmed row"
|
||||
cat, _ = classify.classify(
|
||||
schedule_confirmed_row[COL_WO_STATUS],
|
||||
schedule_confirmed_row[COL_HOLD_REASON],
|
||||
schedule_confirmed_row[COL_LAST_COMMENT],
|
||||
)
|
||||
assert cat == "Schedule Confirmed"
|
||||
|
||||
# Any RCAN row → Cancelled.
|
||||
assert "RCAN" in by_status, "fixture lacks an RCAN row"
|
||||
rcan = by_status["RCAN"][0]
|
||||
cat, _ = classify.classify(
|
||||
rcan[COL_WO_STATUS], rcan[COL_HOLD_REASON], rcan[COL_LAST_COMMENT]
|
||||
)
|
||||
assert cat == "Cancelled"
|
||||
|
||||
# A REPORT-hold row whose comment claims completion → mismatch non-None.
|
||||
if report_completion_mismatch is not None:
|
||||
_, mm = classify.classify(
|
||||
report_completion_mismatch[COL_WO_STATUS],
|
||||
report_completion_mismatch[COL_HOLD_REASON],
|
||||
report_completion_mismatch[COL_LAST_COMMENT],
|
||||
)
|
||||
assert mm is not None
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue